As the methods for automatically generating classification rules from the pre-classified events for classification using this generated rule, a wide variety of methods have hitherto been proposed, such as a statistic method, techniques of knowledge-based processing, techniques based on computational learning theories or neural-net-like techniques. These techniques have their merits and demerits and the suitable area differs problem to problem.
The statistic techniques are principally aimed at analyzing event data based on probabilistic models and hence are used for finding out the main tendency latent in the event data. The volume of processing necessary for rule generation is also small. On the other hand, the statistic techniques are not good at exceptional classifying processing operations. The statistic techniques may be exemplified by quantitation II and Bayesian decision.
The techniques of knowledge-based processing and the computational learning theory are the technique and the theory proposed in the course of researches towards realization of the mechanical learning. Stated briefly, the mechanical learning means autonomous generation by a computer of the adaptive knowledge (information, rules or program etc.). The automatic classification may be classed as a part of this mechanical learning function. While the main object of the statistic technique is discovery of the main tendency of event data, the ultimate object of the mechanical learning is increasing the intelligence of the computer, and hence the object is diversified. The object of the mechanical learning differs slightly from one group of researchers to another. For example, the object of the mechanical learning encompasses finding the extent of hypotheses not in contradiction to the events, recognition and processing of event data behaving exceptionally, or automatic generation of event-generating programs etc.
The neural network type technique is a pseudo-system employing pseudo neural cells and can be applied to learning or pattern recognition. Although the technique can be used easily, the classification rule represents a black-box such that the technique is difficult to be checked or corrected by an operator.
In the following, attention is directed to an inductive inference apparatus (JP Patent Kokoku JP-B-07-43722 or U.S. Pat. No. 4,908,778). This JP Patent Kokoku JP-B-07-43722 is aimed at inductively finding a general knowledge for an event, which comes into being by combining various conditions, from events of the pertinent field in order to acquire the knowledge information required for knowledge processing effectively.
This inductive inference apparatus is fed with a set of case data and subsequently generates a sufficient condition and a necessary condition for realization of respective results of classification. Using these conditions, if an unknown condition is given and the sufficient condition is met, the result of classification is decided to hold (true), whereas, if the unknown condition is given and the necessary condition is not met, the result of classification is judged not to hold.